text: """mobilenetv2.py
  
  This is a copy of:
  https://github.com/ZHKKKe/MODNet/blob/master/src/models/backbones/mobilenetv2.py
  """
  
  
  import math
  import json
  from functools import reduce
  
  import torch
  from torch import nn
  
  
  #------------------------------------------------------------------------------
  #  Useful functions
  #------------------------------------------------------------------------------
  
  def _make_divisible(v, divisor, min_value=None):
      if min_value is None:
          min_value = divisor
      new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
      # Make sure that round down does not go down by more than 10%.
      if new_v < 0.9 * v:
          new_v += divisor
      return new_v
  
  
  def conv_bn(inp, oup, stride):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
          nn.BatchNorm2d(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  def conv_1x1_bn(inp, oup):
      return nn.Sequential(
          nn.Conv2d(inp, oup, 1, 1, 0, bias=False),
          nn.BatchNorm2d(oup),
          nn.ReLU6(inplace=True)
      )
  
  
  #------------------------------------------------------------------------------
  #  Class of Inverted Residual block
  #------------------------------------------------------------------------------
  
  class InvertedResidual(nn.Module):
      def __init__(self, inp, oup, stride, expansion, dilation=1):
          super(InvertedResidual, self).__init__()
          self.stride = stride
          assert stride in [1, 2]
  
          hidden_dim = round(inp * expansion)
          self.use_res_connect = self.stride == 1 and inp == oup
  
          if expansion == 1:
              self.conv = nn.Sequential(
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
                  nn.BatchNorm2d(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup),
              )
          else:
              self.conv = nn.Sequential(
                  # pw
                  nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # dw
                  nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, dilation=dilation, bias=False),
                  nn.BatchNorm2d(hidden_dim),
                  nn.ReLU6(inplace=True),
                  # pw-linear
                  nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                  nn.BatchNorm2d(oup),
              )
  
      def forward(self, x):
          if self.use_res_connect:
              return x + self.conv(x)
          else:
              return self.conv(x)
  
  
  #------------------------------------------------------------------------------
  #  Class of MobileNetV2
  #------------------------------------------------------------------------------
  
  class MobileNetV2(nn.Module):
      def __init__(self, in_channels, alpha=1.0, expansion=6, num_classes=1000):
          super(MobileNetV2, self).__init__()
          self.in_channels = in_channels
          self.num_classes = num_classes
          input_channel = 32
          last_channel = 1280
          interverted_residual_setting = [
              # t, c, n, s
              [1        , 16, 1, 1],
              [expansion, 24, 2, 2],
              [expansion, 32, 3, 2],
              [expansion, 64, 4, 2],
              [expansion, 96, 3, 1],
              [expansion, 160, 3, 2],
              [expansion, 320, 1, 1],
          ]
  
          # building first layer
          input_channel = _make_divisible(input_channel*alpha, 8)
          self.last_channel = _make_divisible(last_channel*alpha, 8) if alpha > 1.0 else last_channel
          self.features = [conv_bn(self.in_channels, input_channel, 2)]
  
          # building inverted residual blocks
          for t, c, n, s in interverted_residual_setting:
              output_channel = _make_divisible(int(c*alpha), 8)
              for i in range(n):
                  if i == 0:
                      self.features.append(InvertedResidual(input_channel, output_channel, s, expansion=t))
                  else:
                      self.features.append(InvertedResidual(input_channel, output_channel, 1, expansion=t))
                  input_channel = output_channel
  
          # building last several layers
          self.features.append(conv_1x1_bn(input_channel, self.last_channel))
  
          # make it nn.Sequential
          self.features = nn.Sequential(*self.features)
  
          # building classifier
          if self.num_classes is not None:
              self.classifier = nn.Sequential(
                  nn.Dropout(0.2),
                  nn.Linear(self.last_channel, num_classes),
              )
  
          # Initialize weights
          self._init_weights()
  
      def forward(self, x):
          # Stage1
          x = self.features[0](x)
          x = self.features[1](x)
          # Stage2
          x = self.features[2](x)
          x = self.features[3](x)
          # Stage3
          x = self.features[4](x)
          x = self.features[5](x)
          x = self.features[6](x)
          # Stage4
          x = self.features[7](x)
          x = self.features[8](x)
          x = self.features[9](x)
          x = self.features[10](x)
          x = self.features[11](x)
          x = self.features[12](x)
          x = self.features[13](x)
          # Stage5
          x = self.features[14](x)
          x = self.features[15](x)
          x = self.features[16](x)
          x = self.features[17](x)
          x = self.features[18](x)
  
          # Classification
          if self.num_classes is not None:
              x = x.mean(dim=(2,3))
              x = self.classifier(x)
  
          # Output
          return x
  
      def _load_pretrained_model(self, pretrained_file):
          pretrain_dict = torch.load(pretrained_file, map_location='cpu')
          model_dict = {}
          state_dict = self.state_dict()
          print("[MobileNetV2] Loading pretrained model...")
          for k, v in pretrain_dict.items():
              if k in state_dict:
                  model_dict[k] = v
              else:
                  print(k, "is ignored")
          state_dict.update(model_dict)
          self.load_state_dict(state_dict)
  
      def _init_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
                  if m.bias is not None:
                      m.bias.data.zero_()
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
              elif isinstance(m, nn.Linear):
                  n = m.weight.size(1)
                  m.weight.data.normal_(0, 0.01)
                  m.bias.data.zero_()
  